Privacy preservation using game theory in e-health application

In new e-health applications, the ubiquitous nature of intelligent devices raises legitimate questions aboutpeoples' privacy, and how to cope with the heterogeneity of user and application requirements in termsof security services. This requires the development of adaptive, context-aware and user-centric securitysolutions. Recent e-health applications (M2M: Machine-to-Machine/IoT: Internet of Things/Web) permit remotemonitoring of patient health, medical treatments, fitness information and parameters, alarm triggering, etc. Asmonitored devices are tightly related to humans, they became able to act on their behalf, and may be modeledas attackers or defenders to make their decision autonomously. In this work, we propose a Markov basedTheory Game Model (MTGM) between a data holder and a data requester in e-health applications to protectdata privacy. The proposed solution ensures the highest payoff through the privacy-preserving decision takento reach a compromise between privacy concession and incentive motivation. Numerical results of executedexperiments are shown to confirm that game theory can enhance privacy protection and improve performanceregarding time efficiency and information loss.